docs: def volume_change_correlation(results, references):
      r"""
      Volume change correlation.
      
      Computes the linear correlation of change in binary object volume between
      the contents of the successive binary images supplied. Measured through
      the Pearson product-moment correlation coefficient. 
      
      Parameters
      ----------
      results : sequence of array_like
          Ordered list of input data containing objects. Each array_like will be
          converted into binary: background where 0, object everywhere else.
      references : sequence of array_like
          Ordered list of input data containing objects. Each array_like will be
          converted into binary: background where 0, object everywhere else.
          The order must be the same as for ``results``.
      
      Returns
      -------
      r : float
          The correlation coefficient between -1 and 1.
      p : float
          The two-side p value.
          
      """
      results = numpy.atleast_2d(numpy.array(results).astype(numpy.bool))
      references = numpy.atleast_2d(numpy.array(references).astype(numpy.bool))
      
      results_volumes = numpy.asarray([numpy.count_nonzero(r) for r in results])
      references_volumes = numpy.asarray([numpy.count_nonzero(r) for r in references])
      
      results_volumes_changes = results_volumes[1:] - results_volumes[:-1]
      references_volumes_changes = references_volumes[1:] - references_volumes[:-1] 
      
      return pearsonr(results_volumes_changes, references_volumes_changes)